Apresentação do Curso
The main goal of this course is to introduce the concept of structural health monitoring (SHM) applied to bridges with hands on experiments. The SHM is posed in the context of a statistical pattern recognition paradigm, rooted in the artificial intelligence field, where machine learning algorithms are essential to perform damage identification, by learning (or modeling) the structural behavior from the experience (past data), following the same principle of the human brain. In order to balance the general concept and the applicability of SHM, this course has been designed to allow students to gain basic knowledge of SHM with hands-on experiences to demonstrate how SHM can be used for assessing the structural condition of bridges.
Direção do Curso
Introduction to SHM in the context of bridge management.
Pose the SHM of bridges in the context of a statistical pattern recognition paradigm.
Overview of sensors and DAQ hardware for designing an optimum instrumentation scheme for SHM.
Overview of finite element modeling and machine learning for feature extraction, data interpretation, and damage identification.
Application of machine learning algorithms for feature modeling and damage identification.
Understand the goal of SHM of bridges with current limitations, grand challenges
The course is tailored towards graduate students and/or practicing engineers working full-time in public and private institutions or consultancy companies.
Conhecimentos, capacidades e competências a adquirir
After successful completing of this course, students will be capable to:
Describe the historical and current real-world applications of damage identification in the bridge field;
Perform damage identification using vibration-based SHM;
Evaluating critically the results of damage identification for quality control;
Choose available commercial software for damage identification analysis.
17:00 às 20:00